This file shows land area flooding at the tract level and county for the state of Georgia. It also pulls out tract locations of high flooding as well as their subsequent counties. It then multiplies tract social vulnerability indicator percentiles by flooding proportion to create a flooding-vulnerability, which is also interactively mapped. Finally, a comparison is drawn between flooded FEMA community lifelines and area flooded by county.

The datasets are the National 100-Year flood layer (2018), CDC SVI indicators (2018), and Re-Public’s database of flooded community lifelines (all from 2018).

These estimates show that as a result of a 100-Year flood, Georgia is 14.79% flooded by land area (which likely includes wetland areas as well). Coastal counties face the brunt of flooding (up to 60% of land area flooded), though the southeastern corner of Georgia as well as various inland tracts also have high risk.

The datasets include 1966 tracts, 1960 of which are mapped. Of those 1966, 117 have null values for flood data. We assume that in the case of missing flood data, the % tract flooded is 0, though these values are graphed as NA at this time.

Tract and County Flooding

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.3     ✓ purrr   0.3.4
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
## Linking to GEOS 3.8.1, GDAL 3.1.4, PROJ 6.3.1
## Loading required package: sp
## rgdal: version: 1.5-23, (SVN revision 1121)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/rgdal/gdal
## GDAL binary built with GEOS: TRUE 
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-5
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.
## Overwritten PROJ_LIB was /Library/Frameworks/R.framework/Versions/4.0/Resources/library/rgdal/proj
## To enable 
## caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.
## Loading required package: carData
## 
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
## 
##     recode
## The following object is masked from 'package:purrr':
## 
##     some
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_double(),
##   STATE = col_character(),
##   ST_ABBR = col_character(),
##   COUNTY = col_character(),
##   LOCATION = col_character()
## )
## ℹ Use `spec()` for the full column specifications.

The following plot shows tract-level land area flooding.

## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'

These following table outlines a list of tracts with flooding close to or above 50%.

## Simple feature collection with 70 features and 2 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -84.98855 ymin: 30.56852 xmax: -80.83973 ymax: 34.26293
## geographic CRS: NAD83
## # A tibble: 70 x 3
##    LOCATION           CensusTractSubmerg…                               geometry
##    <chr>                            <dbl>                     <MULTIPOLYGON [°]>
##  1 Census Tract 9900…               100   (((-81.194 31.57093, -81.18948 31.568…
##  2 Census Tract 9900…               100   (((-81.26668 31.50631, -81.26252 31.5…
##  3 Census Tract 106.…                97.8 (((-84.17031 31.5493, -84.16405 31.55…
##  4 Census Tract 111.…                96.5 (((-80.97446 31.98381, -80.97252 31.9…
##  5 Census Tract 9, G…                94.6 (((-81.49668 31.14877, -81.4847 31.15…
##  6 Census Tract 115,…                89.4 (((-81.24293 31.89407, -81.24142 31.8…
##  7 Census Tract 1.01…                89.1 (((-81.4026 31.13732, -81.39988 31.13…
##  8 Census Tract 111.…                87.0 (((-80.93591 31.96253, -80.93071 31.9…
##  9 Census Tract 111.…                86.8 (((-81.0481 32.0316, -81.04648 32.033…
## 10 Census Tract 8, G…                85.0 (((-81.52685 31.16402, -81.52151 31.1…
## # … with 60 more rows

This dataset includes the flooded area by county, calculated by summing tracts by county.

## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 159 x 2
##    COUNTYFP county_flooded
##    <chr>             <dbl>
##  1 051                60.2
##  2 179                58.2
##  3 191                57.6
##  4 029                56.6
##  5 127                52.4
##  6 299                52.3
##  7 183                51.3
##  8 049                49.6
##  9 065                49.2
## 10 039                45.5
## # … with 149 more rows

The interactive plot below includes county names and subsequent flooding.

## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'

Looking specifically at our 7 counties of analysis: Chatham: 60.169% flooded Liberty: 58.159% flooded McIntosh: 57.641% flooded Stewart: 8.796% flooded Colquitt: 15.611% flooded Rabun: 4.794% flooded Whitfield: 10.099% flooded

Social Vulnerability Indicators and Flooding

For every tract, we calculated a combined social vulnerability-flooding score by multiplying the vulnerability percentile (ranked at the state level) with the tract flooding. This was done for the overall vulnerability percentile, socioeconomic vulnerability percentile, household composition vulnerability percentile, language isolation vulnerability percentile, and housing structure vulnerability percentile.

Below is a table that includes the top 10 most “vulnerable” tracts based on overall flooding and SVI rankings.

## Simple feature collection with 0 features and 4 fields
## bbox:           xmin: NA ymin: NA xmax: NA ymax: NA
## geographic CRS: NAD83
## # A tibble: 0 x 5
## # … with 5 variables: LOCATION <chr>, tract_vuln <dbl>,
## #   CensusTractSubmergedInFlood <dbl>, RPL_THEMES <dbl>, geometry <GEOMETRY
## #   [°]>

This graphic shows the scores from the overall vulnerability percentile.

## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'

This graphic shows the scores from the socioeconomic vulnerability percentile.

## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'

This graphic shows the scores from the household composition vulnerability percentile.

## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'

This graphic shows the scores from the language isolation vulnerability percentile.

## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'

This graphic shows the scores from the housing structure vulnerability percentile.

## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'

Land Area Flooding and Lifeline Flooding

Using Re-Public community lifeline flooding data at the county level, we wanted to assess the relationships between land area flooded and lifeline flooding. Outliers in particular may give an indication of which counties have more pressing mitigation needs related to critical infrastructure, and a relationship between both would encourage more robust flood area analysis and hazard mitigation planning.

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_double()
## )
## ℹ Use `spec()` for the full column specifications.
## Warning: ggrepel: 117 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

There is little correlation between the two parameters (R^2 = 0.08611). While there are 119 points not labeled, those points come in the center. We are especially interested in the outliers, especially ones experiencing high land area flooding or high lifeline flooding. While some of the coastal counties face very flood risk by land area, inland counties with high social vulnerability (ex. Colquitt) may deal with a large loss of critical infrastructure.

## 
## Call:
## lm(formula = county_flooded ~ lifelines_impacted_ratio, data = GA_county_flooding_lifelines)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -19.105  -5.729  -2.366   0.577  43.448 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 3.956      2.520   1.570 0.118417    
## lifelines_impacted_ratio   43.354     11.272   3.846 0.000174 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.22 on 157 degrees of freedom
## Multiple R-squared:  0.08611,    Adjusted R-squared:  0.08029 
## F-statistic: 14.79 on 1 and 157 DF,  p-value: 0.000174